The Complex World of Capital Markets and Market Theory - Essay Example

Running head: Behavioural Finance and the Efficiency of Capital Markets Two Sides of the Same Coin: Behavioural Finance and the Efficiency of Capital Markets Name University Word Count: 3,000 Introduction This essay is a critical analysis of the following quote (WSJ, 2004): "Robert Shiller, a Yale economist, has long argued efficient-market theorists made one huge mistake: 'Just because markets are unpredictable doesn't mean they are efficient. The leap in logic, he wrote in the 1980's, was one of the most remarkable errors in the history of economic thought.' Mr. Fama [Eugene Fama, University of Chicago] says behavioural economists made the same mistake in reverse: 'The fact some individuals might be irrational doesn't mean the market is inefficient.'" The quote refers to two views on stock market behaviour that prominent academics Fama and Shiller have attempted to explain. Fama assumes investors are rational, whilst Shiller assumes the opposite. We can summarise their arguments as follows. Fama (1970) argues that since investors are rational and stock prices behave unpredictably according to available information, he concludes the stock market is efficient. Shiller (1981) argues that since investors are irrational and stock prices behave in partly predictable ways, he concludes the stock market is not efficient. To understand Shiller and Fama better and help us critically evaluate the rationality of their arguments and conclusions, we need to explain their different assumptions and how they arrived at their conclusions. Are Capital Markets Efficient All the literature on market efficiency defines an efficient market as one where prices reflect all available information and sellers cannot earn windfall profits in a sustained manner (Fama, 1970). Large profits can be earned only by having inside information that is not publicly known and trading based on such information, or through misinformation; both are illegal. In an efficient market, assuming all companies disclose information to investors, only those who enter the market first may earn above average returns. Just like any other market, the one who arrives first can buy at a lower price and then, as demand goes up, sell at a higher price. This logic that consistently beating the market is not possible led to the creation of index funds that mimic the market's performance. Nevertheless, small investors unaware of these academic and empirical discussions continue to try to beat the market, only to incur expenses on fees and commissions. Behavioural finance proponents think that market-beating strategies exist and that a careful analysis of historical price trends and financial reports can pay off (Shiller, 1990). They point to stock market anomalies and other forms of market inefficiencies that allow investors to reap above average returns. So going back to our question: are markets efficient Fama (1998) thinks it is and that it continues to be so as proven by empirical studies (Fama and French 1992, 1993, 1996 and Malkiel, 1995). He claims that conclusions based on market anomalies discovered by behavioural finance are due to poorly done statistical work (1998, pp. 292-294) and amateurish techniques (1998, p. 296). He cited (1998, pp. 288-290) above average returns as the result of chance, that behavioural finance models are loaded with judgmental biases making it predictably easy to justify any hypothesis proposed, and that the efficient market hypothesis can explain all forms of market behaviour to date. Behavioural finance supporters Barberis, Shleifer and Vishny (1998) claim that an ongoing battle between rational and irrational traders exists in the market, with the irrational ones dominating. The systematic errors that irrational investors make when they use public information to form expectations of future cash flows overwhelm the efforts of rational traders to undo the former's market dislocating effects. Daniel, Hirshleifer, and Subrahmanyam (1998) state that irrational traders' overconfidence in interpreting private information pushes up prices above rational fundamentals and increases market inefficiency. Behavioural finance studies are also backed by empirical evidence, Fama's comments notwithstanding, that cite limits to arbitrage (Shleifer and Vishny, 1997) and behavioural psychology (Shiller, 2000), both individual (Shleifer, 2000) and collective (Hirshleifer and Teoh, 2003), as factors that explain inefficient market behaviour. Turning Point and Current State The main event that made critics doubt the validity of capital markets efficiency was Black Monday, 19 October 1987, when the Dow Jones Industrial Average dropped 22 percent, the largest one-day drop in the index in forty-five years. What caused it when no substantial information on the market's fundamentals was released the weekend before the crash A subsequent study by Mitchell and Netter (1989) cited evidence in support of a plausible reason that caused the crash. Supporters of efficient markets (Fama, 1998) declared it was proof of their thesis, with rational investors anticipating the effects of legislation, whilst behavioural finance supporters (Shiller, 2000; Poterba and Summers, 1988; Zeckhauser, Patel, and Hendricks, 1991; Hirshleifer and Teoh, 2003) claim it as proof of irrational behaviour, with both rational and irrational investors over-reacting to anticipated effects of legislation and, therefore, proving the irrationality and inefficiency of capital markets. Despite the voluminous amount of literature on the topic, both efficient markets and behavioural finance proponents agree that their models have not managed to fully explain capital markets behaviour (Fama, 2001; Thaler and Barberis, 2001). If we were to gauge the battle by peer recognition, however, it would seem that behavioural finance are ahead, its main proponents Kahneman and Smith having won Nobel Prizes in 2002 and Shiller being awarded the American Economics Association gold medal the same year. Fama has not yet won any awards. Analysis With an overview of each side's assumptions and conclusions, and an idea of what their peers think about their work, we proceed with our analysis. We review our understanding of what a capital market is, how the prices of stocks and bonds traded in capital markets are determined, and using the papers cited, show how the two theories explain capital markets behaviour. A capital market is a venue where buyers and sellers agree to buy and sell stocks and bonds without the use of financial intermediaries like banks and insurance companies who direct the flow of resources from savers to borrowers. Capital market transactions are therefore deemed efficient in the absence of intermediaries except for brokers who put buyers and sellers together and get a small commission, making the deal almost frictionless. This is one factor that leads to the efficient market hypothesis: low transaction costs enhance trading efficiency. With transaction costs negligible, the only real factor that determines the current price of a stock should be the net present value of its future cash flows in the form of dividends and, assuming the company lasts long enough, capital gains when the stock is sold at a future date. After all, a stock is nothing else but a claim to a company's future cash flows. A company's cash flow is affected by several factors, such as business prospects, management quality, the economy's over-all performance, and the company's past performance. If all these information are known, computing for free cash flow looks relatively straightforward, and using a discount rate, the stock's present value can be easily calculated. If the market price is lower than the present value, the stock is bought. Otherwise, if one is holding the stock, it is sold. This is where the two theories reach a collision point. If the price reflects all available information, why did the seller sell and the buyer buy Obviously, the seller sold because he thinks it is overvalued, and the buyer thought the opposite. The behaviourists argue that if the seller knew something the buyer didn't and the buyer knew something the seller didn't, the market is inefficient; and if a seller, thinking the price will fall, finds a willing buyer, and they go ahead with the transaction, then both must be irrational. Not so fast, say Fama and efficient market proponents. Their original model (Fama, 1970) accounts for three sets of information (historical prices, public, and private information), resulting in three types of efficiency (weak, semi-strong, and strong, depending on the combination of information sets the investor possesses). Since buyers and sellers may have different sets of information, they behave rationally according to the information they have. Buy and sell transactions can be easily explained: a seller unaware of publicly available information may have used historical prices and perceived a decline, whilst a buyer had both public and private information and expected an increase in price. The market, therefore, is efficient because the price of a stock will reflect all available information. To further our understanding of stock market behaviour, we can recall Zeno's mathematical paradox, which states that one never reaches one's destination. The reasoning is simple: to get to your destination, you have to get half way, and then to negotiate the remaining half, one needs to again get half way. Therefore, since distance is a distinct quantity that one has to keep on dividing by half, we never get to our destination. But the fact is we do, which is why it is a paradox: the mental model of dividing distances by half works mathematically, but it does not adequately explain reality. Behavioural finance and efficient capital markets are like that: mathematical models that give us an idea of how markets work but may not fully explain why it works that way. Both camps admit this much in their papers: some investors are rational whilst others are irrational, markets are affected by rational and irrational behaviours, and markets behave predictably or unpredictably. Shiller's quote on Fama's flawed leap of logic is actually one way of looking at market behaviour, whilst Fama's rebuttal is, essentially, looking at the market from the other side: market irrationality does not necessarily mean inefficiency. In fact, if we analyse their statements closely, Fama and Shiller are like the proverbial blind men defining a beast differently but saying the same thing: the market is efficient in the long run but in the short run shows signs of inefficiencies that allow above average returns. Why it behaves this way we do not exactly know why, and neither do they. The key that unlocks our understanding of the main differences in viewpoint leading to the disagreement between the two schools is the time frame of observation. Behavioural finance theorists analyse market unpredictability and short-run inefficiencies and attribute it to irrationality, whilst efficient market theorists assume market unpredictability and short-term inefficiencies and attribute it to different information sets. In other words, both theories offer plausible explanations for market behaviour, but neither on its own explains it fully. Combine both and you understand markets better. Conclusions If as the behaviourists claim the capital markets are inefficient, they would come up against a brick wall: why do stocks continue to be traded and how would asset pricing look like The fact that stocks and bonds are traded means that the psychological barriers that accompany behavioural finance theories of market behaviour have to end at some point. Otherwise, if everyone agrees that everybody else is irrational, then the prices of stocks are imaginary, unreliable, and therefore not credible. There are easier ways to make money, and the rational traders (behavioural finance believes that some traders are rational) should have abandoned the market. That buyers and sellers agree on the prices of stocks and bonds to be traded is a proof similar to Zeno's paradox: at some point, the behavioural model gives way to the real world. Each theory provides a model that is useful for analysing the complex world of capital markets. Efficient market theory helps us understand how information influences asset prices, whilst behavioural finance helps us understand how irrational behaviour influences asset prices. The first provides a stable foundation for market transactions to take place, whilst the second gives us an explanation as to why market anomalies like asset bubbles, market crashes, calendar effects, size effects, under- and over-reaction, herd behaviour, endorsement effect, and many others of a similar ilk take place. William Sharpe, a 1990 Nobel Prize winner and supporter of both theories, best reflects the current outlook when he said: "As a practical matter, I still think it's prudent to assume that the market is pretty close to efficient in terms of pricing and risk and return and all that. On the other hand, we've certainly learned from cognitive psychology that ordinary human beings need to have alternatives framed in ways that can help them make right decisions rather than wrong decisions. (Fox, J., 2002, p. 59)" Sharpe's comments give us a clear idea on the state of this ongoing intellectual battle. In the end, the investor who grows in his understanding of capital markets would benefit from these academic discussions. And for as long as investors continue to behave either rationally or irrationally, there will be sufficient material for future research work by academics on this topic. The January Effect One of the stock anomalies cited by behavioural finance as proof of market inefficiency is the so-called January effect that can be stated simply as "stock prices tend to go up in January" (Gultekin and Gultekin, 1983). Thaler (1987) and Shiller (1997) attributed this to psychological factors, whereby investors allowed themselves to be influenced by their own mental compartments. The anomaly could not be explained in terms of effects related to the tax year, since it comes up in the UK (tax year begins in April) and Australia (tax year begins in July). If people view the year-end as a time of reckoning and a new year as a new beginning, they may be inclined to behave differently, explaining the January effect (Shiller, 1997). Dimson and Marsh (1999) showed that for UK stocks during the period 1955-2000, there is no evidence of a calendar effect in the UK, suggesting that these results are opposite that of the evidence in the U.S. We will disprove this shortly. Table 1 displays the results of calculating the monthly price increases of the FTSE 100 from April 1984 to January 2006 based on downloaded data (GFD, 2006), indicating the absence of price increase anomalies in January after 1989, except for 1998. Table 2 shows results of recalculations for the same sample, grouping them into seven test periods to determine a January effect, starting in 1984, the year after Gultekin and Gultekin (1983) published their paper on the anomaly. The results are strikingly consistent with both efficient markets and behavioural finance theories, showing that information affects market efficiency, that markets are rational, and that markets are predictable. Note that from the period 1984-1994, when the January effect was a relatively new finding, the spike in January prices was obvious. The stock price increase during the month of January was 3.39 percent, followed by February at 1.39 percent and then April at 1.13 percent. In 1989 it increased by 14.44 percent in January! Note that the farther away we go from 1984, the January effect diminishes and a shift can be noted towards a higher increase in the month of December. What may account for this Traders, expecting higher than normal returns in January, began accumulating stocks in December. The increased demand then pushed up the stock price ahead of January trading, moving the anomaly to the month of December. Table 2 shows that the January effect has disappeared (though 2006 showed an increase of 3.26 percent) and the anomaly has shifted to April (UK tax year begins) with an increase of 2.45 percent during the period January 2001 to January 2006 and to October, with an increase of 2.87 percent. This also hints that the spread of information on the anomaly diminished the probability of earning above average stock returns. In Figure 1, we show that after 1998, the cumulative increase for January/December is lower than the April/October. Figure 2 displays trend lines for the cumulative increase showing that the April/October increase is sloping upwards, opening the possibility of making profits from this new anomaly. Is the April/October Anomaly a Market-beating Strategy This information hints at two ways to beat the market. Using the 2001-2006 trend of a 2.45 percent increase in FTSE 100 stocks in April and a 2.87 percent increase in October, one can bet on the index, buying an index fund tied to the FTSE 100 towards the end of March and selling it when it exceeds the expected return of 2.45 percent sometime in April. The same can be done in September and October. This, however, may be already known by almost everyone, and we who are familiar with the two theories should expect other investors who know how to calculate stock returns to think and act similarly. The other way is to play around with the composition of the index, and there are many ways of doing so using P/E ratios, historical price movements, stock beta, and other variables. Since it is not the purpose of this paper to have an accurately diversified portfolio of stocks, we limit ourselves to conducting a simple exercise. Using market capitalisation (for our computations, we used the FTSE ASWB dated 30 November 2005), we divided the FTSE 100 index into quintiles and compared their March to April and September to October returns. Table 3 shows the average monthly increases from 2001 to 2006 based on our stock market data. Note that the increases of Stock Groups B and D are higher than the increases of Groups A, C, and E, with Group B stocks returning 4.42 percent in March-April and 5.17 percent in September-October and Group D stocks returning 3.53 percent and 4.14 percent for the same period. In theory, anyone who invests only in the forty stocks contained in these two groups bounded by the market capitalisation figures may beat the market, as the portfolio is not weighed down by the lower returns from other stock groups in the index, giving returns that may be potentially higher than the index. How certain are we that it will work out Behavioural finance theorists who believe in the predictability of market anomalies tell us we have a good chance of beating the market. Efficient market theorists consider this a weak form of efficiency from historical data and can only wish us luck. There is only one way to find out. References Barberis, N. & Thaler, R. (2001). A survey of behavioural finance. Retrieved 25 February 2006, from http://introduction.behaviouralfinance.net/BaTh01.pdf Barberis, N., Shleifer, A., & Vishny, R. (1998). A model of investor sentiment. Journal of Financial Economics 49, 307-343. Daniel, K., Hirshleifer, D., & Subrahmanyam, A. (1998). Investor psychology and security market under- and overreactions. Journal of Finance 53, 1839-1885. Dimson, E., and Marsh P. R. (1999). Murphy's law and market anomalies. Journal of Portfolio Management 25(2): 53-69. Fama, E. (1970). Efficient capital markets: a review of theory and empirical work. Journal of Finance, 25, 383-417. Fama, E. F. (1998). Market efficiency, long-term returns, and behavioural finance. Journal of Financial Economics 49, 283-306. Fama, E., & French, K. (1992). The cross-section of expected stock returns. Journal of Finance 47, 427-465. Fama, E., & French, K. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3-56. Fama, E., & French, K. (1996). Multifactor explanations of asset pricing anomalies. Journal of Finance 51, 55-84. Fama, E. & French, K. (2001). Disappearing dividends: changing firm characteristics or lower propensity to pay Journal of Financial Economics, 60, 3-43. Fox, J. (2002). Is the market rational No, say the experts. But neither are you--so don't go thinking you can outsmart it. Fortune Magazine. New York: Time-Warner, 9 December, pp. 55-59. FTSE ASWB (All-Share Index Weightings Book) (2005). London Stock Exchange and Financial Times. 30 November 2005. GFD (Global Financial Data) (2006). Retrieved 26 February 2006, from https://www.globalfinancialdata.com Gultekin, M. & Gultekin, N.B. (1983). Stock market seasonality: international evidence. Journal of Financial Economics, 12: 469-481. Hirshleifer, D. & Teoh, S. H. (2003). Herd behaviour and cascading in capital markets: a review and synthesis. European Financial Management, Vol. 9, No. 1, 25-66. Malkiel, B. G. (1995). Returns from investing in equity mutual funds 1971 to 1991. Journal of Finance, 50, 549-72. Mitchell, M. & Netter, J. (1989). Triggering the 1987 stock market crash: antitakeover provisions in the proposed House Ways and Means tax bill Journal of Financial Economics, 24: 37-68. Poterba, J. & Summers, L. (1988). Mean reversion in stock prices: evidence and implications. Journal of Financial Economics 22: 27-59. Shiller, R. (1981). The use of volatility measures in assessing market efficiency. Journal of Finance, 36: 291-304. Shiller, R. (1990). Market volatility and investor behavior. American Economic Review, 80(2) 58-62. Shiller, R. (1997). Human behaviour and the efficiency of the financial system. Paper presented at a conference on "Recent Developments in Macroeconomics" at the Federal Bank of New York, 27-28 February 1997. Shiller, R. (2000). Irrational exuberance, Princeton, NJ: Princeton University Press. Shleifer, A. (2000). Inefficient markets: an introduction to behavioral finance. Oxford: Oxford University Press. Shleifer, A., & Vishny, R. (1997). Limits of arbitrage. Journal of Finance 52, 35-55. Thaler, R. H. (1987). "The January effect," Journal of Economic Perspectives, 1(1): 197-201. WSJ (Wall Street Journal) (2004). Vol. XXII, No. 182. Zeckhauser, R., Patel, J, & Hendricks, D. (1991). Nonrational actors and financial market behavior. Working Paper No. 3731. Cambridge, MA: NBER. Table 1. Monthly price increase (in percent) over previous month for FTSE 100 stocks. Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 2006 3.26 2005 0.79 2.39 (1.49) (1.89) 3.38 3.01 3.31 0.28 3.41 (2.93) 1.99 3.60 2004 (2.65) 2.31 (2.37) 2.37 (1.31) 0.75 (1.14) 1.04 2.51 1.17 1.71 2.36 2003 (9.47) 2.50 (1.18) 8.65 3.11 (0.42) 3.12 0.10 (1.68) 4.80 1.28 3.86 2002 (1.01) (1.24) 3.35 (2.78) (0.79) (8.43) (8.81) (0.45) (11.96) 8.54 3.21 (5.49) 2001 1.21 (6.03) (4.80) 5.91 (2.86) (2.65) (2.01) (3.33) (8.26) 2.78 3.25 0.27 2000 (9.55) (0.57) 4.94 (3.25) 0.50 (0.73) 0.83 4.83 (5.67) 2.56 (4.85) 1.31 1999 0.23 4.73 1.95 4.08 (4.98) 1.48 (1.37) 0.23 (3.47) 3.75 5.46 5.05 1998 6.29 5.66 2.86 (0.07) (0.97) (0.65) 0.08 (10.07) (3.52) 7.38 5.62 2.41 1997 3.82 0.76 0.11 2.85 4.18 (0.36) 6.58 (1.83) 8.86 (7.66) (0.22) 6.29 1996 1.90 (0.84) (0.75) 3.19 (1.84) (0.98) (0.21) 4.44 2.23 0.64 1.98 1.49 1995 (2.41) 0.59 4.27 2.51 3.19 (0.14) 4.49 0.42 0.87 0.60 3.83 0.68 1994 2.15 (4.69) (7.26) 1.26 (4.95) (1.73) 5.60 5.47 (6.92) 2.35 (0.52) (0.52) 1993 (1.38) 2.17 0.37 (2.28) 0.98 2.09 0.91 5.93 (2.02) 4.40 (0.13) 7.94 1992 3.13 (0.35) (4.76) 8.77 2.02 (6.88) (4.82) (3.63) 10.40 4.12 4.53 2.44 1991 1.25 9.70 3.18 1.21 0.53 (3.39) 7.21 2.20 (0.91) (2.12) (5.68) 3.01 1990 (3.52) (3.50) (0.33) (6.43) 11.49 1.26 (2.04) (7.02) (7.98) 3.02 4.83 (0.27) 1989 14.44 (2.42) 3.63 2.07 (0.17) 1.73 6.79 3.96 (3.71) (6.82) 6.26 6.41 1988 4.56 (1.23) (1.49) 3.43 (0.99) 4.10 (0.22) (5.39) 4.16 1.42 (3.24) 0.04 1987 7.70 9.46 0.92 2.65 7.44 3.68 3.36 (4.71) 5.17 (26.04) (9.71) 8.41 1986 1.59 7.59 8.09 (0.50) (3.47) 2.93 (5.56) 6.62 (6.34) 4.91 0.27 2.59 1985 3.98 (1.52) 1.28 1.10 1.70 (5.95) 2.17 6.29 (3.81) 6.76 4.49 (1.84) 1984 (10.69) 2.22 (2.87) 9.30 3.26 1.12 2.52 4.24 Source: Computed from Global Financial Data (2006) Table 2.a. Comparison of calendar effects for different test periods Average Month-on-Month Returns for FTSE 100 stocks Test Periods Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 1995-2006 (0.63) 0.93 0.62 1.96 0.15 (0.83) 0.44 (0.39) (1.52) 1.97 2.12 1.98 2001-2006 (1.31) (0.01) (1.30) (b) 2.45 0.31 (1.55) (1.11) (0.47) (3.20) (b) 2.87 2.29 0.92 1996-2000 (c) 0.54 1.95 1.82 1.36 (0.62) (0.25) 1.18 (0.48) (0.32) 1.33 1.60 (c) 3.31 1984-1994 (a) 3.39 1.52 0.36 1.13 0.35 0.01 0.96 1.73 (0.79) (0.63) 0.33 2.95 1991-1995 0.55 1.48 (0.84) 2.29 0.35 (2.01) 2.68 2.08 0.29 1.87 0.41 2.71 1984-1990 (a) 4.79 1.40 2.02 0.39 0.76 1.43 0.23 1.29 (1.32) (2.23) 0.78 2.80 1984-2006 1.20 1.21 0.50 1.57 0.25 (0.41) 0.70 0.67 (1.15) 0.67 1.22 2.47 Source: Computed from Global Financial Data (2006) Notes: (a) January effect during the period 1984-1994 with lower returns in 1991-1995. (b) Highest returns during the last five years (2001-2006) in April and October. (c) Shift in calendar effect to December during the period 1996-2000. Table 3. Proposed Market-beating strategy. Market Cap ( Million) Increase (%) Min Max Mar-Apr Sep-Oct 12,574 132,270 2.96 3.46 6,223 12,350 4.42 5.17 4,231 6,156 1.01 1.18 3,149 4,080 3.53 4.14 1,313 3,047 0.33 0.39 Average Returns 2.45 2.87 Sources: FTSE ASWB (Nov 2005); GFD (2006). Assemble a portfolio bounded by the following Market Capitalisation boundaries Company Name Mkt Cap ( Million) A BP 132,270 Prudential 12,574 B Imperial Tobacco 12,350 Xstrata 6,223 C Old Mutual 6,156 Scottish and Newcastle 4,231 D Friends Provident 4,080 Liberty International 3,149 E Enterprise Inns 3,047 Antofagasta 1,313 Source: FTSE ASWB (Nov 2005) Figure Captions Figure 1. 20-year Percentage Increases over Previous Month for January, April, October, and December. Figure 2. Trend Lines for January-December and April-October Share Price Behaviour. Figure 1. Figure 2. Read More